In this tutorial we introduce the linear birth-death process as a statistical model for cutting through stochasticity in diversification rates. We also introduce LiteRate, an unsupervised machine-learning algorithm built on birth-death processes designed to identify statistically-signifcant shifts in diversification rates (Silvestro et al., 2019). Finally, we show users how to run LiteRate on their own data. Empirically, the module introduces the diversification of Metal bands active from 1968-2000 as a means to understand the history of the Metal music genre.
How to start this tutorial
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This project was supported by Grant #61105 from the John Templeton Foundation to the University of Tennessee, Knoxville (PIs: S. Gavrilets and P. J. Richerson) with assistance from the Center for the Dynamics of Social Complexity and the National Institute for Mathematical and Biological Synthesis at the University of Tennessee, Knoxville.
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